Classifying next-gen opportunities in ETFs

Jul 1st, 2020 | By | Category: Equities

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By Matthew J. Bartolini, Head of SPDR Americas Research, State Street Global Advisors.

Matthew J Bartolini, Head of SPDR Americas Research

Matthew J Bartolini, Head of SPDR Americas Research.

The Covid-19 pandemic has created a new trend line for our society. From changes to our daily routines, to how we pay for things, to how we consume media and stay connected, our lives have been disrupted.

Some of these trends were already in place before the pandemic, but now, these changes are likely to be amplified as we transition to a more digitally connected (yet physically separate) world.

A tectonic shift of this magnitude may create tangible opportunities, and investors are likely to seek out specific exposure tools to capture one or all of these next-generation (NextGen) trends.

As a result, thematic ETFs are likely to become more popular than those that adhere to the market-cap-weighted archetype or traditional sector frameworks. And over the last few weeks more have been filed, with some launched. But thematic ETFs and their portfolio construction can vary significantly.

Due diligence is key as these ETF offerings are spread over such an extensive range. The first step in due diligence is to understand what strategies are in scope, and that requires classification. Creating a classification scheme, however, is the windmill that ETF nerds, like me, constantly tilt at. And thematic ETF classification is the mother of all windmills and a true (don) quixotic journey.

Classifying thematic ETFs

Systematic classification is always the goal. In a blog post, I discussed how we created a systematic classification for smart beta strategies. I took the same approach with thematic ETFs, and it almost broke me. I first tried to use Bloomberg’s characteristic data, which is notoriously robust, but failed: it was impossible to systematically classify exposures based on market available data (i.e., market cap, sector, and industry information) AND underlying portfolio traits (i.e., weighting scheme, holdings, etc.). Corner solutions emerged quickly. Funds that should be included were not, and others snuck in – like a traditional technology sector ETF. Every time I tinkered with the code, another problem sprung up. It was whack-a-mole coding.

After hours of trying to engineer a process, I abandoned it in favor of a more qualitative approach that required dissecting all 2,000-plus funds listed in the US. But some quantitative analysis was required, so I culled the list by removing any funds that were easy to exclude (i.e., traditional sector ETFs, as well as broad market cap beta equity, fixed income, and commodity exposures).

To identify the funds, I leveraged applicable portions of New Economies framework developed by our partner S&P Kensho and grouped them into 12 thematic categories based on their fund objective:

  • Broad Innovation: Innovation throughout the economy
  • Clean Energy: Renewables or firms with low carbon footprints
  • Cloud Computing: Cloud storage and cloud-based software
  • Democratized Banking: Digital payments and encrypted banking technology
  • Final Frontiers: Space and deep-sea exploration
  • Future Communications: 5G networks, streaming media, and videogames
  • Future Security: Cybersecurity and drone technology
  • Human evolution: Advanced medicines and health care solutions
  • Intelligent Infrastructure: Smart cities, power grids, and water technology
  • New Consumer: e-Commerce and gig economy
  • Robotics & AI: Robotics & AI as well as advanced manufacturing
  • Smart Mobility: Ridesharing and autonomous vehicles

The result was the identification of 137 funds, comprising $36 billion of assets focused on these particular NextGen trends. As shown below, most of the assets are in funds classified as Broad Innovation and Clean Energy. Clean Energy gets a boost from some legacy ESG ETF positions. However, since the pandemic, there has been noticeable interest over the last few months in Cloud Computing and Future Communications – outside of just broad innovation.

Source: SSGA.

NextGen trends performance potential

While the flows are concentrated in certain areas, the performance trends are actually quite dispersed among the categories. In 2020, the average return for thematic NextGen ETFs was 6%, besting the S&P 500 Index by 9%. However, the dispersion across the space was 71%! The best performance for any of the thematic ETFs identified was an eye-popping 48%, yet the worst return was -23%. The fund with the best return in 2020 resided in Cloud Computing, while the worst return was found in Democratized Banking. However, some funds in Cloud Computing produced negative returns year-to-date in 2020, while certain strategies in Democratized Banking have registered positive returns. This discrepancy is an indication of how theme selection and a fund’s portfolio construction process are crucial.

While the performance range is wide-spread, the number of funds in each bucket outperforming the broader market has been high. So far, in 2020, 64% of all NextGen trend funds beat the S&P 500 with 82% outperforming over the last three months. The categories that had the highest hit rate year-to-date, and over the past three months (i.e., a period covering when the world changed) are Broad Innovation, Future Security, and Cloud Computing, as shown in the chart below.

Source: SSGA.

But performance comes at a cost. The average fee for these NextGen trends funds is 57 basis points – 20% higher than the average fee charged on all ETFs. Performance also comes with higher volatility, as every category has a higher average standard deviation of returns than the S&P 500 over the last 12 months. These ETFs also have a high level of concentration, as the median number of holdings is 52. The median market cap weight within each fund is $34 billion – a full 90% below the S&P 500 and an indication of a noticeable size bias in these portfolios. Lastly, from a valuation perspective, these exposures are more skewed towards growth than value, given a higher median price-to-book and price-to-earnings for the portfolios compared to the S&P 500.

Understanding the potential and choosing the right tool

The pandemic has led to an inflection point that may present NextGen opportunities that are not currently well represented in traditional market exposures. But choosing single stocks for exposure to this new wave of innovation comes with significant risk: not all “innovative” firms innovate successfully. A diversified investment approach that is non-market cap-weighted, like a thematic ETF, is optimal when targeting thematic NextGen trends.

There is, of course, a risk, of getting the theme right, but using the wrong implementation tool. To get both right requires an understanding of:

  • Security selection (i.e., how the stocks get into the funds)
  • Weighting scheme (i.e., equal-weighted or weighted based on fundamental metrics related to the theme)
  • Inherent sector biases (tech-heavy or industrial-focused)
  • Market cap profile (i.e., non-market cap-weighted funds increase any size bias)
  • Performance trends over this period (i.e., where many of the themes came to life)

All facets are important in seeking to position portfolios for a post-Covid-19 world with NextGen trend ETFs. Not to mention, what actually classifies as a NextGen trend ETF!

(The views expressed here are those of the author and do not necessarily reflect those of ETF Strategy.)

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